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Comparison of different optimization and process control procedures

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2010
DOI: 
doi:10.3926/jiem.2010.v3n2.p383-398

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
This paper includes a comparison of different optimization methods, used for optimizing the cutting conditions during milling. It includes also a part of using soft computer techniques in process control procedures. Milling is a cutting procedure dependent of a number of variables. These variables are dependent from each other in consequence, if we change one variable, the others change too. PSO and GA algorithm are applied to the CNC milling program to improve cutting conditions, improve end finishing, reduce tool wear and reduce the stress on the tool, the machine and the machined part. At the end a summary will be given of pasted and future researches.
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PDF icon arastirmax-comparison-different-optimization-and-process-control-procedures.pdf
  • 2
383-398
  • English

REFERENCES

References: 

Baskar, N., & Asokan, P. (2005). Optimization of machining parameters for milling operations using non-conventional methods. International Journal of Advance Manufacturing Technology, 25, 1078-1088. doi:10.1007/s00170-003-1939-9
Chandrasekaran, M., Muralidhar, M., Murali Krishna, C., & Dixit, U.S. (2010). Application of soft computing techniques in machining performance prediction and optimization: a literature review. International Journal of Advance Manufacturing Technology, 46, 445-464. doi:10.1007/s00170-009-2104-x
Dixit, P.M. (2008). Modeling of metal forming and machining processes: by finite element and soft computing methods. Springer: London.
Dorigo, M. (1996): The ant system: optimization by a colony of cooperating agents. IEEE Transnational System Management Cybernetics, 26, 1–13.
Finnie, I. (1956). Review of the metal-cutting analysis of the past hundred years. Mechanical Engineering, 78, 715–721.
Hsueh, Y.-W., & Yang, C.-Y. (2009). Tool breakage diagnosis in face milling by support vector machine. Journal of Materials Processing Technology, 209(1), 145-152. doi:10.1016/j.jmatprotec.2008.01.033
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia.
Socha, K., & Dorigo, M. (2008). Ant colony optimization for continuous domain. European Journal of Operation Research, 185, 1155–1173. doi:10.1016/j.ejor.2006.06.046
Valentan, B., Brajlih, T., Drstvensek, I., & Balic, J. (2006). Evaluation of shape complexity based on STL data. Journal of achievements in materials and manufacturing engineering, 17, 1-2.Wong, K.Y., & Komarudin (2008). Parameter tuning for ant colony optimization: a review. Proceedings of the international conference on computer and communication engineering 2008, Kuala Lumpur, Malaysia.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/S0019-9958(65)90241-X
Zarei, O., Fesanghary, M., Farshi, B., Jalili, S.R., & Razfar, M. R. (2009). Optimization of multi-pass face-milling via harmony search algorithm. Journal of Materials Processing Technology, 209, 2386–2392. doi:10.1016/j.jmatprotec.2008.05.029

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